Heart disease (HD) remains one of the leading causes of mortality in the world and there are pressing needs to develop effective predictive models of HD at the early stages of development. The given research suggests a new type of computational framework combining optimization-based machine learning with innovative preprocessing and optimization approaches to the design of models predicting the presence of heart disease. It is an analysis of the data collection having clinically meaningful patient attributes. First, the dataset is cleaned with the help of the Discrete Wavelet Transform (DWT) to minimize noise and normalize signals to provide data of the necessary quality and make them more ready to use in a model. In feature extraction, Principal Component Analysis (PCA) algorithm is used to diminish dimensions and maintain most important variables in the realm of heart disease decision. After feature extraction, the data undergoes a Neural Network model which is optimized by Real-Parameter Numerical Optimization Algorithm (ROA). Since ROA has a high convergence rate and stability in locating global solutions, it is selected. As a way of enhancing the model, hyperparameter tuning- Bayesian optimization, is used to increase the overall predictive accuracy. Key performance indicators of the model include: accuracy 97%, precision 95%, Sensitivity 94% and specificity92%. The provided system shows good performance in all of them, which proves its potential in achieving robust and early prediction of heart diseases. The proposed neural network model optimized by ROA has greater gains as compared to other algorithms like a Hippopotamus Optimization (HO) and Puma Optimization (PO) algorithms.
Keywords
HD Prediction, Optimization Algorithms, ROA, HO Algorithm, PO Algorithm, DWT, External Parameter Tuning, Statistical Analysis.
S. Qiao et al., “SPReCHD: Four-Chamber Semantic Parsing Network for Recognizing Fetal Congenital Heart Disease in Medical Metaverse,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 6, pp. 3672–3682, Jun. 2024, doi: 10.1109/jbhi.2022.3218577.
B. Ramesh and K. Lakshmanna, “A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System,” IEEE Access, vol. 12, pp. 26683–26695, 2024, doi: 10.1109/access.2024.3366537.
H. K. Alkahtani, I. U. Haq, Y. Y. Ghadi, N. Innab, M. Alajmi, and M. Nurbapa, “Precision Diagnosis: An Automated Method for Detecting Congenital Heart Diseases in Children From Phonocardiogram Signals Employing Deep Neural Network,” IEEE Access, vol. 12, pp. 76053–76064, 2024, doi: 10.1109/access.2024.3395389.
N. A. Vinay, K. N. Vidyasagar, S. Rohith, D. Pruthviraja, S. Supreeth, and S. H. Bharathi, “An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process,” IEEE Access, vol. 12, pp. 65482–65502, 2024, doi: 10.1109/access.2024.3397574.
C. Yin, Y. Zheng, X. Ding, Y. Shi, J. Qin, and X. Guo, “Detection of Coronary Artery Disease Based on Clinical Phonocardiogram and Multiscale Attention Convolutional Compression Network,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1353–1362, Mar. 2024, doi: 10.1109/jbhi.2024.3354832.
T. Jumphoo, K. Phapatanaburi, W. Pathonsuwan, P. Anchuen, M. Uthansakul, and P. Uthansakul, “Exploiting Data-Efficient Image Transformer-Based Transfer Learning for Valvular Heart Diseases Detection,” IEEE Access, vol. 12, pp. 15845–15855, 2024, doi: 10.1109/access.2024.3357946.
S. Hietakoste et al., “Acute Cardiorespiratory Coupling Impairment in Worsening Sleep Apnea-Related Intermittent Hypoxemia,” IEEE Transactions on Biomedical Engineering, vol. 71, no. 1, pp. 326–333, Jan. 2024, doi: 10.1109/tbme.2023.3300079.
D. Y. Omkari and K. Shaik, “An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers,” IEEE Access, vol. 12, pp. 56275–56290, 2024, doi: 10.1109/access.2024.3389707.
S. Mondal, R. Maity, Y. Omo, S. Ghosh, and A. Nag, “An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches,” IEEE Access, vol. 12, pp. 7255–7270, 2024, doi: 10.1109/access.2024.3350996.
M. Alkhodari, L. J. Hadjileontiadis, and A. H. Khandoker, “Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 1803–1814, Apr. 2024, doi: 10.1109/jbhi.2024.3357506.
A. B. Naeem et al., “Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach,” IEEE Access, vol. 12, pp. 37349–37362, 2024, doi: 10.1109/access.2024.3373646.
H. Zhang, P. Zhang, Z. Wang, L. Chao, Y. Chen, and Q. Li, “Multi-Feature Decision Fusion Network for Heart Sound Abnormality Detection and Classification,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1386–1397, Mar. 2024, doi: 10.1109/jbhi.2023.3307870.
X. Zhang et al., “An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation,” IEEE Transactions on Medical Imaging, vol. 43, no. 2, pp. 723–733, Feb. 2024, doi: 10.1109/tmi.2023.3319720.
L. Lu et al., “Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review,” IEEE Reviews in Biomedical Engineering, vol. 17, pp. 180–196, 2024, doi: 10.1109/rbme.2023.3271595.
K. Radha, M. Bansal, and R. Sharma, “Raw Waveform-Based Custom Scalogram CRNN in Cardiac Abnormality Diagnosis,” IEEE Access, vol. 12, pp. 13986–14004, 2024, doi: 10.1109/access.2024.3356075.
S. Das and S. Dandapat, “Heart Murmur Severity Stages Classification Using Multikernel Residual CNN,” IEEE Sensors Journal, vol. 24, no. 8, pp. 13019–13027, Apr. 2024, doi: 10.1109/jsen.2024.3373226.
T. Ullah et al., “Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection,” IEEE Access, vol. 12, pp. 16431–16446, 2024, doi: 10.1109/access.2024.3359910.
A. T. Shumba et al., “Monitoring Cardiovascular Physiology Using Bio-Compatible AlN Piezoelectric Skin Sensors,” IEEE Access, vol. 12, pp. 16951–16962, 2024, doi: 10.1109/access.2024.3359058.
S. Ling et al., “A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 7, pp. 4036–4047, Jul. 2024, doi: 10.1109/jbhi.2024.3390688.
J. Li et al., “Thrombus Detection in the Artificial Heart Pump Based on Electrical Impedance Spectroscopy,” IEEE Sensors Journal, vol. 24, no. 11, pp. 18372–18381, Jun. 2024, doi: 10.1109/jsen.2024.3387920.
A. Jiménez-Partinen, K. Thurnhofer-Hemsi, J. Rodríguez-Capitán, A. I. Molina-Ramos, and E. J. Palomo, “Coronary Artery Disease Classification with Different Lesion Degree Ranges Based on Deep Learning,” IEEE Access, vol. 12, pp. 69229–69239, 2024, doi: 10.1109/access.2024.3401465.
D. Enériz et al., “Low-Cost FPGA Implementation of Deep Learning-Based Heart Sound Segmentation for Real-Time CVDs Screening,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–16, 2024, doi: 10.1109/tim.2024.3392271.
Q. Qi, X. Han, and Y. Liu, “Application Status and Prospect of Deep Learning in Echocardiography,” IEEE Access, vol. 12, pp. 83405–83416, 2024, doi: 10.1109/access.2024.3413681.
A. Lekha, C. V. Srikrishna, and V. Vinod, “Fuzzy Association Rule Mining,” Journal of Computer Science, vol. 11, no. 1, pp. 71–74, Jan. 2015, doi: 10.3844/jcssp.2015.71.74.
N. RajKumar and V. Vinod, “Integrated Educational Information Systems for Disabled Schools via a Service Bus using SOA,” Indian Journal of Science and Technology, vol. 8, no. 13, Jul. 2015, doi: 10.17485/ijst/2015/v8i13/55030.
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Conceptualization: Vinitha R, Viji Vinod, Sharmila K P and Kavithavarshini G S;
Writing- Original Draft Preparation: Vinitha R, Viji Vinod, Sharmila K P and Kavithavarshini G S;
Visualization: Vinitha R and Viji Vinod;
Investigation: Vinitha R, Viji Vinod, Sharmila K P and Kavithavarshini G S;
Supervision: Sharmila K P and Kavithavarshini G S;
Validation: Vinitha R and Viji Vinod;
Writing- Reviewing and Editing: Vinitha R, Viji Vinod, Sharmila K P and Kavithavarshini G S; All authors reviewed the results and approved the final version of the manuscript.
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Vinitha R
Department of Computer Science, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India.
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Cite this article
Vinitha R, Viji Vinod, Sharmila K P and Kavithavarshini G S, “A Novel Computational Model to Predict Healthcare Problems Through Machine Optimization and Hyperparameter Tuning Logic”, Journal of Machine and Computing, vol.5, no.4, pp. 2211-2223, October 2025, doi: 10.53759/7669/jmc202505171.
